Package {ExpDesignR}

Type: Package
Title: Experimental Design and Randomization Methods for Biomedical and Veterinary Research
Version: 0.1.0
Description: Generates randomized experimental designs for biomedical, veterinary, agricultural, and clinical research, including simple, block, stratified, and cluster randomization, Latin square and crossover designs, allocation summaries, schedule export, and visualization of treatment allocations. The methods are based on established principles of randomization and experimental design; see Rosenberger and Lachin (2015, ISBN:9781118742242) and Jones and Kenward (2014, ISBN:9781439861424).
License: MIT + file LICENSE
Encoding: UTF-8
Depends: R (≥ 4.2.0)
Imports: stats, utils, tibble, dplyr, ggplot2, rlang
Suggests: testthat (≥ 3.0.0), covr, spelling, knitr, rmarkdown
URL: https://github.com/vinodhpmd/ExpDesignR
BugReports: https://github.com/vinodhpmd/ExpDesignR/issues
Language: en-US
Config/testthat/edition: 3
NeedsCompilation: no
Config/roxygen2/version: 8.1.0
VignetteBuilder: knitr
Packaged: 2026-08-21 07:53:39 UTC; m
Author: Vinodhkumar Obli Rajendran [aut, cre], Keerthi Aaradhana [aut]
Maintainer: Vinodhkumar Obli Rajendran <vinodhkumar.rajendran@gmail.com>
Repository: CRAN
Date/Publication: 2026-08-27 10:30:08 UTC

Allocation Summary

Description

Summarizes treatment allocations from a randomization schedule.

Usage

allocation_summary(schedule, group_col = "Group")

Arguments

schedule

A data frame or tibble produced by an ExpDesignR randomization function.

group_col

Name of the treatment group column.

Value

A tibble summarizing the number and percentage of subjects in each treatment group.

Examples

sch <- simple_randomization(
  n = 20,
  groups = c("Control","Treatment"),
  seed = 123
)

allocation_summary(sch)

Block Randomization

Description

Generates a randomized allocation schedule using fixed block randomization.

Usage

block_randomization(n, groups, block_size = 4, seed = NULL)

Arguments

n

Total number of subjects.

groups

Character vector of treatment groups.

block_size

Size of each block. Must be a multiple of the number of treatment groups.

seed

Optional random seed.

Value

A tibble with subject allocation.

Examples

block_randomization(
  n = 24,
  groups = c("Control","Treatment"),
  block_size = 4,
  seed = 123
)

Cluster Randomization

Description

Randomly assigns clusters (e.g., villages, farms, schools, hospitals) to treatment groups.

Usage

cluster_randomization(clusters, groups, seed = NULL)

Arguments

clusters

Character or numeric vector of cluster IDs.

groups

Character vector of treatment groups.

seed

Optional random seed.

Value

A tibble containing cluster assignments.

Examples

cluster_randomization(
  clusters = paste0("Farm_", 1:20),
  groups = c("Control", "Treatment"),
  seed = 123
)

Crossover Design

Description

Generates a crossover design for clinical, veterinary, pharmaceutical and agricultural experiments.

Usage

crossover_design(
  treatments,
  subjects,
  periods = length(treatments),
  seed = NULL
)

Arguments

treatments

Character vector of treatment labels.

subjects

Number of subjects.

periods

Number of study periods.

seed

Optional random seed.

Value

A tibble containing the crossover schedule.

Examples

crossover_design(
  treatments = c("A","B"),
  subjects = 8,
  periods = 2,
  seed = 123
)

Export Randomization Schedule

Description

Export a randomization schedule to a CSV file.

Usage

export_schedule(schedule, file, row.names = FALSE)

Arguments

schedule

A data frame or tibble generated by ExpDesignR.

file

Character. Output CSV filename or path. This argument must be supplied explicitly.

row.names

Logical. Should row names be written?

Value

Invisibly returns the input schedule unchanged. The function writes the schedule to the CSV file specified by file.

Examples

sch <- simple_randomization(
  n = 20,
  groups = c("Control", "Treatment"),
  seed = 123
)

tf <- tempfile(fileext = ".csv")

export_schedule(
  sch,
  file = tf
)

unlink(tf)

Latin Square Design

Description

Generates a Latin Square design for experimental studies.

Usage

latin_square(treatments, randomize = TRUE, seed = NULL)

Arguments

treatments

Character vector of treatment labels.

randomize

Logical. Should rows, columns and treatments be randomized? Default is TRUE.

seed

Optional random seed.

Value

A matrix representing a Latin square.

Examples

latin_square(
  treatments = LETTERS[1:4],
  seed = 123
)

Plot Randomization Schedule

Description

Creates a bar chart showing the number of subjects allocated to each treatment group.

Usage

plot_randomization(
  schedule,
  group_col = "Group",
  fill = "#2C7FB8",
  title = "Treatment Allocation"
)

Arguments

schedule

A data frame produced by ExpDesignR.

group_col

Character. Name of the treatment column.

fill

Character. Fill colour.

title

Character. Plot title.

Value

A ggplot object.

Examples

sch <- simple_randomization(
  n = 40,
  groups = c("Control","Treatment"),
  seed = 123
)

plot_randomization(sch)

Simple Randomization

Description

Generate a simple random allocation schedule.

Usage

simple_randomization(n, groups, seed = NULL)

Arguments

n

Number of subjects.

groups

Character vector of treatment groups.

seed

Optional random seed.

Value

A tibble containing subject IDs and assigned groups.

Examples

simple_randomization(
  n = 20,
  groups = c("Control", "Treatment"),
  seed = 123
)

Stratified Randomization

Description

Generates a randomized allocation schedule within each stratum.

Usage

stratified_randomization(data, strata, groups, seed = NULL)

Arguments

data

A data frame containing the study subjects.

strata

Character vector specifying one or more stratification variables.

groups

Character vector of treatment groups.

seed

Optional random seed.

Value

A tibble containing the original data with an additional treatment allocation column.

Examples

df <- data.frame(
  ID = 1:20,
  Sex = rep(c("Male","Female"), each = 10),
  Age = rep(c("Young","Adult"), times = 10)
)

stratified_randomization(
  data = df,
  strata = c("Sex"),
  groups = c("Control","Treatment"),
  seed = 123
)